Controlling the Trade-Off between Resource Efficiency and User Satisfaction in NDNs Based on Naïve Bayes Data Classification and Lagrange Method

Abstract

This paper addresses the fundamental problem of the trade-off between resource efficiency and user satisfaction in the limited environments of Named Data Networks (NDNs). The proposed strategy is named RADC (Resource Allocation based Data Classification), which aims at managing such trade-off by controlling the system’s fairness index. To this end, a machine learning technique based on Multinomial Naïve Bayes is used to classify the received contents. Then, an adaptive resource allocation strategy based on the Lagrange utility function is proposed. To cache the received content, an adequate content placement and a replacement mechanism are enforced. Simulation at the system level shows that this strategy could be a powerful tool for administrators to manage the trade-off between efficiency and user satisfaction.

Keywords

• named data network
• cache strategy
• machine learning
• placement strategy
• name classification
• resource allocation